Machine Learning Engineer

The Role

You make models run. Not in a notebook. Not on a demo. In production, on real hardware, at the edge of what's possible within a split-horizon GPU/CPU architecture. You optimize inference, you manage memory budgets measured in gigabytes, and you understand the difference between a model that works in a lab and a model that works in someone's home.

What You'll Do

• Optimize model training and inference pipelines for the MABOS architecture
• Implement and tune models for split-horizon GPU/CPU execution
• Develop quantization, pruning, and distillation strategies for MABOS-MINI
• Build and maintain the ML infrastructure that powers both local and cloud deployments
• Profile and eliminate performance bottlenecks across the full inference stack

Requirements

• MS or PhD in machine learning, or equivalent demonstrated through shipped production ML systems
• 5+ years deploying models in production environments with real constraints
• Deep GPU expertise. CUDA or ROCm, memory optimization, and inference acceleration
• Quantization experience. Model quantization and edge deployment
• You've shipped ML systems that run on hardware you couldn't control

Compensation

All positions include equity in Ceridwen.ai. Salaries are TBD and will be determined based on role scope, experience, and what you bring to the table. We will not insult you with a lowball offer, and we expect you not to waste our time with inflated expectations disconnected from contribution.

The Builder Clause

We don't care where you went to school. We don't care if you went to school. Our founder is self-taught, started coding at 13, and built a 602,000-line cognitive architecture without a CS degree.

Meet the qualifications, or show us what you've built. Either path works. Both paths demand excellence.

Apply

Ready to move? Send a short note of relevant proof-of-work — past shipped projects, metrics you moved, or a draft of your first 30 days.